Looking under the hood of Tech Nation 2016: process, findings and

lessons

Juan Mateos-GarciaJuan.mateos-garcia@nesta.org.uk

READIE Research Summit, 22 March 2016

Goals & Structure• Goals of the session:– Having a practical opportunity to think about how to combine

data opportunities and policy needs– Talk to each other!– Create the seed of an amazing project that will change the

face of the digital economy in Europe• I’ll get you in the mood with a little talk about Tech Nation 2016,

a policy-relevant project to understand the digital tech economy in the UK:– Origin– Process– Impacts– Lessons

Origin

2010 Feb 2016Feb 2015~2013

GOAL: Produce a rigorous, timely map of the UK digital tech industries that speaks to a policy and industry audience. Nesta = Analytical partner.

Design featuresGOAL CHALLENGE SOLUTION

Timely, industry relevant analysis of the digital tech industries

SIC codes aren’t sufficient to do this

Use web data to identify digital tech

businesses

Policy relevant analysis including economic

measurements

Public/open data doesn’t include econ

measures

Use official micro-data and a transparent

methodology to select SICs

Consider digitisation of non-digital sectors

Few datasets measure business digitisation

Select datasets with combinations of

sector/digital capability

Measure skills base in local ecosystems

Skills supply is both formal and informal

Use web data to measure local skills and

networks

Data and research team

Nesta team included Hasan Bakhshi (lead), Juan Mateos-Garcia (quant analysis), Madeleine Gabriel (qual analysis), Cath Sleeman (interactive data viz), Matthew Williams (external, supported work with GI, GitHub, Meetup data).Analytical work August 2015-January 2016 -> VERY TIGHT.

• This includes ONS (BSD, ABS, APS) data some of which was obtained by Frontier Economics.

• We also collected HESA, RCUK data.

• TCUK also ran a business survey

• 40 qual interviews.

Briefly, on definitions

Definition of digital tech based on previous Nesta analysis, dynamic mapping method:• Based on intensity of digital

tech occupations in different industries.

• Allows distinguishing between digital tech industries and jobs, and look at digitisation in non-digital.

Impacts

Big media coverage• 39,000 visits• 8 national pieces

(Guardian, FT etc.) • 31 regional pieces,

56 trade pieces• 5 broadcasts.

Impacts (2)

Lessons & next steps• It’s not about big data OR official data OR surveys. It can (and

probably should) be all.• It’s easy to be overwhelmed by data. Use domain knowledge to

identify interesting questions to prioritise (working with TCUK was helpful for this).

• Give yourself enough time to analyse the data.• Segment outputs (headlines vs. technically complex info).What’s next?• We are now moving from static to interactive, and descriptive to

predictive.• Considering policy implications• …And starting to think about next year’s Tech Nation!

Follow-up!

• Juan.mateos-garcia@nesta.org.uk • @JMateosGarcia• https://www.linkedin.com/in/juanmateosgarcia